Faculty Contact Information:
Name: Craig Ewert
E-mail: cewert@faculty.ed.umuc.edu
Office hours: will be set when classes begin
E-mail is normally checked daily, and responses will be made when the e-mail is checked. Note: this may be no more than an acknowledgment of receipt. Technical or personal difficulties may, on occasion, delay this. Physical items (letters, disks) may also be left in my UMUC mailbox.
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Consultation:
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Consultation should be performed during office hours. Extended time may be requested, either during class breaks or via e-mail. You should consider consulting with other classmates as a first step.
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Required Texts and Readings:
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Turban, E., and Aronson, J. (2001). Decision Support Systems and Intelligent Systems (6th ed.). Saddle River, NJ: Prentice Hall.
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Supplementary Readings:
The standard for papers in the graduate program is the APA style. All participants in this course and all graduate INSS, MGMT, PUAD, and ECON courses should have a copy of the style guide:
American Psychological Association. (2001). Publication Manual of the American Psychological Association, 5th Edition. Washington DC: Author.All graduate students should be prepared to utilize the UMUC online library at http://www.umuc.edu/library/. The library contains a large number of full text academic journals that are free of charge and immediately available. The library homepage also contains a number of links related to improving students' research and writing skills.
Additionally, there are a fairly large number of web sites dedicated to some aspect of AI, and several Usenet newgroups dealing with the subjects. You are expected to use these resources in this class.
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Recommended Journals:
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Publications of the various professional societies (such as ACM -- the Association for Computing Machinery, the IEEE Computing Society, and the various management professional societies) are strongly recommended. In addition, there are many trade journals (such as eWEEK) that MIS professionals should become familiar with, many of these being published both weekly and on-line.
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Course Description:
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3 semester hours credit. Prerequisite: Either INSS 510, INSS 520, or permission of the Program Director. Examines human information processing capabilities and limitations as they relate to the design, development, and implementation of information systems. Artificial intelligence methodologies for the emulation and enhancement of human information processing are examined. Expert system, neural net, and natural language processing are discussed
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Course Goals:
Upon completion of the course, participants should:
1. Understand the place of AI in cognitive science
2. Have an appreciation for the definitional difficulties
3.· Be able to define the Turing test, and discuss why it may not be sufficient
4. Understand and describe how Eliza-style systems work.
5.· Understand the capabilities and limitations of artificial neural networks
6.· Understand and be able to discuss the architecture of ANNs, and differentiate between models
7.· Understand the capabilities and limitations of expert systems
8.· Understand the design and usage of cellular automata
9.· Be able to use ANNs, CAs, and/or expert systems
10.· Understand some of the links between AI and artificial life
11.· understand some applications of genetic algorithms
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Course Objectives:
After completing this course, the student will be able to:
1. Define and discuss the phases of the decision making process
2. Research and report on major trends in a global marketplace that affect managerial decision making (information literacy, effective writing, international perspective, historical perspective)
3. Identify the characteristics and benefits of Decision Support Systems (DSS) (competence in information technology)
4. Define and illustrate with examples management science, compare structured, semi-structured, and unstructured decisions, and list and classify major decision support models
5. Define and identify characteristics of a data warehouse (competence in information technology)
6. Research and report on selection criteria for obtaining DSS software (competence in information technology, information literacy, effective writing)
7. List the characteristics, benefits, and challenges of groupwork and available supporting groupware (competence in information technology
8, Compare and contrast executive information systems (EIS) and DSS (competence in information technology)
9. Discuss intellectual assets, the characteristics and benefits of knowledge management, and the challenges and strategies for successful knowledge management (competence in information technology)
10. Define, trace the development of, and illustrate current uses of expert systems (ES), intelligent agents, and neural networks(competence in information technology, historical perspective)
11. Assess and report on domestic and global issues related to privacy and security of data and information required for use in DSS, EIS, ES (competence in information technology, civic responsibility, international perspective, information literacy, effective writing) .
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Grading Information:
Grades for this course will be assigned as follows:
A 92%
B 80 – 91%
C 70 – 79%
F Below 70%
Please note that Bowie State University does not use "D" for graduate students. The grade F(a) is used to designate academic failure. F(n) is used to designate failure for non-completion. Grades of Incomplete or Withdrawal are governed by UMUC-Europe policies. For further details, please refer to the UMUC-Europe Graduate Catalog, available in your local Education Center or online at http://www.ed.umuc.edu/general_info/publications/catalogs.
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Course Requirements:
Graduate school at the masters level focuses on helping students obtain the education needed for success as professionals in their chosen fields. Thus, UMUC-Europe Graduate Programs and Bowie State University share the common goals of promoting excellence in academic scholarship through thoughtful inquiry and the skillful application of knowledge and theory for the betterment of society.
In order to maximize your graduate educational experience in general and this course in particular, you are required to:
20% - Participate in/lead classroom discussions
40% - Complete graduate level projects or programming assignments, write graduate level papers or case studies
30% - Orally/visually present prepared material
10% - Complete one or more written examination(s)>
(subject to change depending upon class size)
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Description of Course Requirements:
Participate in classroom discussions: You are expected to come to class prepared to engage in all discussions in a professional and informed manner. Usually this requires two to three hours for every hour of a face-to-face class and approximately ten hours of preparation per week for a DE class.
Complete graduate level projects or programming assignments, write graduate level papers or case studies: You are required to conduct professional-level research, including appropriately citing works of others and avoiding plagiarism. Plan on committing approximately 150 hours over the duration of this course to producing professional level deliverables, to include programs, projects, papers, and/or case studies.
Orally/visually present prepared material: You are required to present your results in a professional manner. In a face-to-face course, this typically means an oral presentation accompanied by appropriate visual material. In a DE class, this means creating a visual/textual presentation for your instructor and classmates.
Complete one or more written examination(s): The examination process in this class will assist you in developing the writing and critical thinking skills necessary for successfully passing the comprehensive exam required of all graduate students. The examination questions used for this course will either be taken directly from past comprehensive exams or written as though to be included on a comprehensive exam.
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Course Schedule:
This schedule presents 16 units or modules, with each unit corresponding to a regular three-hour weekday meeting, a half-day on weekends, or a full week of DE. It is tentative. The precise nature of each meeting topic will be impacted by the size of the group.
Initial meeting:
Introductions
Review of syllabus
Clarification of goals, objectives and requirements
Orientation to subject
Discussion of chapters 1 & 2
Second meeting: Chapters 3-4
Third meeting: Chapters 15-17
Fourth meeting: Chapters 15-17 cont.
Fifth meeting: Chapter 10
Sixth meeting: Chapter 11, selection of projects, initial software choices made
Seventh meeting: Chapter 12, and NLP
Eigth meeting: Chapter 13
Ninth meeting: Chapter 14
Tenth meeting: Chapter 5
Eleventh meeting: Chapter 6
Twelfth meeting: Chapters 7 & 8
Thirteenth meeting: Chapter 18
Fourteenth meeting: Chapter 19
Fifteenth meeting:
Examination
Student presentations
Sixteenth meeting:
Student presentations
Course evaluations
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Academic Policies:
Please refer to the UMUC - Europe Graduate Catalog, available online at http://www.ed.umuc.edu/general_info/publications/catalogs/index.html or from your local Education Center, for information on the following:
Academic Integrity
Course Load
Exception to Policy
Grade Appeal Process
Make-up Examinations
Nondiscrimination
Students with Disabilities.
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Faculty Bio:
Biographical info:
Mr. Ewert received his BS in mathematics from Illinois Institute of Technology in 1969, and began working in the computing field that year. He returned to academia in the 1990's, obtaining an Associates Degree in Electronic Engineering Technology from his local community college in 1992, and a Masters degree in Computer Science from Roosevelt University in Chicago in 1998. He is currently pursuing a Ph.D. degree in Computer Science at DePaul University in Chicago, specializing in Artificial Intelligence. His research interests center around genetic algorithms, artificial neural networks, and cellular automata, with application to computer vision.
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